Hydroelectric generator dynamic regulation and control method and system based on environment change

Abstract

The invention relates to the technical field of hydroelectric generator regulation and control, in particular to a hydroelectric generator dynamic regulation and control method and system based on environment change, which improve the operation efficiency and stability of the hydroelectric generator in a complex and changeable environment and enhance the safety and economy. The method comprises the following steps: according to a set acquisition frequency, continuously acquiring environment data information of a place where the hydroelectric generator is located; performing feature extraction on the environment data information acquired each time by using a preset trained key feature extraction model to obtain an environment key feature vector; performing fluctuation analysis on a plurality of continuous environment key feature vectors to obtain an environment fluctuation index; comparing the environmental fluctuation index with a preset fluctuation threshold value; and in response to the environment fluctuation index exceeding a preset fluctuation threshold value, inputting the latest acquired environment key feature vector into the hydroelectric generator environment influence analysis model to obtain a hydroelectric generator environment influence index.

Description

Technical Field

[0001] The present invention relates to the technical field of hydroelectric generator regulation, and in particular to a method and system for dynamically regulating a hydroelectric generator based on environmental changes. Background Art

[0002] In the field of renewable energy, hydroelectric generators, as the core equipment of hydropower generation, have a direct impact on the power supply quality and economic benefits of the power system due to their operating efficiency and stability; however, the operating environment of hydroelectric generators is complex and changeable, including various natural factors such as water flow velocity, water level, water temperature, air humidity, temperature and wind speed, all of which have a significant impact on the operating efficiency and safety of hydroelectric generators.

[0003] Existing methods for controlling the operation of hydroelectric generators often rely on fixed operating parameter settings or simple adjustments based on manual experience, which makes it difficult to accurately respond to complex and changing environmental changes in real time. For example, when the water flow rate suddenly increases, if the speed or blade angle of the hydroelectric generator is not adjusted in time, it may cause the unit to overload or even cause mechanical failure; conversely, if the water flow rate decreases and the output is not reduced in time, it will cause energy waste. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a method and system for dynamic control of a hydroelectric generator based on environmental changes, which improves its operating efficiency and stability in a complex and changeable environment while enhancing its safety and economy.

[0005] In a first aspect, the present invention provides a method for dynamically controlling a hydroelectric generator based on environmental changes, the method comprising:

[0006] Continuously obtain environmental data information of the location of the hydroelectric generator according to the set collection frequency;

[0007] Using the preset trained key feature extraction model, feature extraction is performed on the environmental data information collected each time to obtain the key feature vector of the environment;

[0008] Perform fluctuation analysis on multiple consecutive key environmental feature vectors to obtain the environmental fluctuation index;

[0009] Comparing the environmental fluctuation index with a preset fluctuation threshold;

[0010] In response to the environmental fluctuation index exceeding a preset fluctuation threshold, the newly acquired environmental key feature vector is input into the hydroelectric generator environmental impact analysis model to obtain the hydroelectric generator environmental impact index;

[0011] The environmental impact index of the hydroelectric generator is optimized in the preset operation mode mapping space to obtain the optimal operation mode of the hydroelectric generator under the real-time environmental conditions; and the operation parameters of the hydroelectric generator are adjusted according to the optimal operation mode;

[0012] In response to the environmental fluctuation index not exceeding the preset fluctuation threshold, the real-time operation mode of the hydroelectric generator is maintained unchanged.

[0013] Furthermore, the environmental fluctuation index calculation formula is:

[0014]

[0015] Wherein, FI represents the environmental fluctuation index, reflecting the average degree of fluctuation; V i Represents the key feature vector of the environment at the i-th time point; ∥V i+1 -V i ∥ means V i+1 and V i The Euclidean distance between them is used to calculate the difference between two vectors; n represents the number of data points in the time window.

[0016] Furthermore, the method for constructing the key feature extraction model includes:

[0017] Collect data on the operating status of hydroelectric generators under different environmental conditions;

[0018] Clean the collected data to remove outliers, missing values, and noise data;

[0019] Selecting a deep learning model as the infrastructure of the key feature extraction model; the deep learning model includes a convolutional neural network, a recurrent neural network, a support vector machine, and an autoencoder;

[0020] Use the processed data as input to train the key feature extraction model;

[0021] Use an independent test set to evaluate the performance of the model;

[0022] Tune the model based on the evaluation results;

[0023] The trained feature extraction model is deployed to the dynamic control system of the hydroelectric generator.

[0024] Furthermore, the influencing factors for setting the preset fluctuation threshold include the physical characteristics of the hydroelectric generator, the range and frequency of changes in environmental factors, the demand and stability of the power system, and the safety performance and reliability.

[0025] Furthermore, the method for constructing the hydroelectric generator environmental impact analysis model comprises:

[0026] Collect historical data sets containing environmental data and hydroelectric generator operation data;

[0027] Preprocess the collected data, including removing outliers, filling missing values, and standardizing and normalizing the data;

[0028] Selecting a machine learning model as the infrastructure of the hydroelectric generator environmental impact analysis model; the machine learning model includes linear regression, random forest regression, gradient boosting tree, multi-layer perceptron, convolutional neural network and recurrent neural network;

[0029] Use the preprocessed dataset to train the selected model and adjust the model’s hyperparameters;

[0030] An independent test set is used to validate the model and evaluate its generalization ability on unknown data;

[0031] Tune the model based on the validation results;

[0032] The model is deployed to the actual hydropower generator operation and control system.

[0033] Furthermore, the method for constructing the preset operation mode mapping space includes:

[0034] Identify the operating requirements and limitations of hydroelectric generators under different environmental conditions;

[0035] Collect the operating data of the hydroelectric generator under different environmental conditions, including environmental parameters and corresponding operating parameters; pre-process the collected data to remove outliers and missing values;

[0036] According to the operation requirements and limitations of the hydroelectric generator, the operation modes are divided into different categories, and corresponding operation parameter settings are defined for each mode;

[0037] The key feature vector of the environment is used as the input space, the operation mode is used as the output space, and a mapping relationship from the input space to the output space is constructed;

[0038] Establish the mapping relationship between the key feature vectors of the environment and the operating mode, and construct the operating mode mapping space.

[0039] Furthermore, factors influencing the setting of the acquisition frequency include environmental change rate, system response requirements, data processing capabilities, cost-effectiveness, historical data analysis, and field conditions.

[0040] On the other hand, the present application also provides a dynamic control system for a hydroelectric generator based on environmental changes, the system comprising:

[0041] The environmental data collection module continuously obtains environmental data information of the location of the hydroelectric generator according to the set collection frequency;

[0042] The feature extraction module uses the preset trained key feature extraction model to extract features from the environmental data information collected each time and obtain the key feature vector of the environment;

[0043] The environmental fluctuation analysis module performs fluctuation analysis on multiple consecutive environmental key feature vectors to obtain the environmental fluctuation index;

[0044] A threshold comparison module compares the environmental fluctuation index with a preset fluctuation threshold;

[0045] The environmental impact analysis module, in response to the environmental fluctuation index exceeding the preset fluctuation threshold, inputs the latest acquired environmental key feature vector into the hydroelectric generator environmental impact analysis model to obtain the hydroelectric generator environmental impact index;

[0046] The operation mode optimization module optimizes the environmental impact index of the hydroelectric generator in the preset operation mode mapping space to obtain the optimal operation mode of the hydroelectric generator under the real-time environmental conditions; and adjusts the operation parameters of the hydroelectric generator according to the optimal operation mode; in response to the environmental fluctuation index not exceeding the preset fluctuation threshold, the real-time operation mode of the hydroelectric generator is kept unchanged.

[0047] In a third aspect, the present application provides an electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and the computer program, when executed by the processor, implements the steps of any one of the above methods.

[0048] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps in any one of the above-mentioned methods when executed by a processor.

[0049] Compared with the prior art, the present invention has the following beneficial effects: the method continuously acquires environmental data by setting the acquisition frequency, and immediately analyzes and processes the data, so that the system can quickly respond to environmental changes and adjust the operating state of the hydroelectric generator in time, thereby effectively avoiding problems such as overload, failure and energy waste;

[0050] By using the key feature extraction model and environmental impact analysis model, the intelligent analysis and processing of complex environmental factors are realized, which can accurately extract key features and predict the impact of environmental changes on hydropower generators, so as to make scientific decisions;

[0051] Automatically adjust the operating mode of the hydroelectric generator according to the real-time environmental status, without manual intervention or simple adjustment based on fixed parameters; enable the hydroelectric generator to maintain the best operating state under different environmental conditions, improve operating efficiency and stability; by accurately adjusting the operating parameters of the hydroelectric generator, ensure that the unit operates under the optimal working conditions, reduce unnecessary energy waste, and improve power generation efficiency;

[0052] Timely response to environmental changes avoids mechanical failures and safety accidents caused by overload operation, and improves the safety and reliability of hydropower generators; intelligent and automated control methods reduce the frequency of manual operation and intervention, and reduce operation and maintenance costs and human resource investment;

[0053] In summary, this dynamic control method significantly improves the operating efficiency and stability of the hydropower generator in a complex and changing environment by controlling its operation in real time, intelligently and adaptively, while also enhancing its safety and economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a flow chart of the present invention;

[0055] Figure 2 is a flow chart of the method for building a key feature extraction model;

[0056] Figure 3 It is a structural diagram of the dynamic control system of a hydroelectric generator based on environmental changes. DETAILED DESCRIPTION

[0057] In the description of the present application, those skilled in the art should know that the present application can be implemented as a method, an apparatus, an electronic device, and a computer-readable storage medium. Therefore, the present application can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), a combination of hardware and software. In addition, in some embodiments, the present application can also be implemented in the form of a computer program product in one or more computer-readable storage media, and the computer-readable storage medium contains computer program code.

[0058] The above-mentioned computer-readable storage medium may adopt any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or devices, or any combination of the above. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, optical disc read-only memories, optical storage devices, magnetic storage devices, or any combination of the above. In the present application, computer-readable storage media can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or device.

[0059] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws.

[0060] The present application describes the provided methods, devices, and electronic devices through flowcharts and / or block diagrams.

[0061] It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, and these computer-readable program instructions are executed by a computer or other programmable data processing device to produce a device that implements the functions / operations specified by the boxes in the flowchart and / or block diagram.

[0062] These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to work in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product including functions / operations specified in the blocks in the flowchart and / or block diagram.

[0063] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable data processing apparatus to provide a process for implementing the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0064] The present application is described below in conjunction with the drawings in the present application.

[0065] Embodiment 1: Figure 1 to Figure 2 As shown, the method for dynamic control of a hydroelectric generator based on environmental changes of the present invention specifically comprises the following steps:

[0066] S1. Continuously obtain environmental data information of the location of the hydroelectric generator according to the set collection frequency;

[0067] The environmental data information includes but is not limited to natural factors such as water flow velocity, water level, water temperature, air humidity, temperature and wind speed; high-precision sensors are deployed around the hydroelectric generator to monitor key environmental indicators such as water flow velocity, water level, water temperature, air humidity, temperature and wind speed; the sensors should have good reliability and anti-interference capabilities to ensure the accuracy of data collection;

[0068] According to actual needs and system design requirements, set an appropriate acquisition frequency. The acquisition frequency should not be too low to capture environmental changes, nor too high to cause unnecessary computing burden. The acquisition frequency needs to be determined through experimental verification to ensure that it can respond quickly to environmental changes without excessively consuming resources.

[0069] The data collected by the sensor must be transmitted to the central control system through reliable communication means and stored in the database for subsequent processing; the security and integrity of the data must be ensured during data transmission;

[0070] The factors affecting the setting of the acquisition frequency include:

[0071] Environmental change rate: Different environmental factors have different change rates. Water flow velocity may change rapidly due to rainfall, reservoir water release, etc., while water temperature, air humidity, etc. may change relatively slowly. Therefore, the acquisition frequency needs to be adjusted according to the actual change rate of each environmental factor. For environmental factors that change rapidly, a higher acquisition frequency should be set to ensure timely response. For environmental factors that change slowly, the acquisition frequency can be appropriately reduced to reduce the amount of data processing.

[0072] System response requirements: The response speed requirements of the hydroelectric generator operation and control system are also an important consideration for setting the acquisition frequency. If the system needs to respond quickly to environmental changes to adjust the operating status of the hydroelectric generator, then a higher acquisition frequency needs to be set. The system's processing power and data transmission speed also need to be considered to ensure that the real-time and accuracy of the data can be maintained under high-frequency acquisition.

[0073] Data processing capability: After data is collected, it needs to be processed and analyzed to extract key features and make decisions. The system's data processing and analysis capabilities will limit the setting of the collection frequency. If the system's processing capacity is limited, too high a collection frequency may cause data processing delays or losses, affecting the control effect. Therefore, it is necessary to set a reasonable collection frequency based on the actual processing capacity of the system.

[0074] Cost-effectiveness: The setting of the acquisition frequency also needs to consider cost-effectiveness; high-frequency acquisition will increase the burden on sensors, data storage and processing equipment, thereby increasing costs; on the premise of meeting the system response requirements, the acquisition frequency should be reduced as much as possible to save costs; at the same time, the impact of data acquisition accuracy and reliability on the overall benefits of the system also needs to be considered;

[0075] Historical data analysis: By analyzing historical environmental data, we can understand the changing patterns and characteristics of various environmental factors and provide a reference for setting the collection frequency;

[0076] Site conditions: Site conditions such as the installation location, accuracy, and stability of the sensor will also affect the setting of the acquisition frequency; the performance of sensors and other data acquisition equipment is also a factor that needs to be considered when setting the acquisition frequency; high-performance equipment can support higher acquisition frequencies without affecting data quality.

[0077] In this step, by setting a suitable acquisition frequency, the system can ensure that it can capture subtle changes in the environment around the hydropower generator in a timely manner, which helps to improve the stability of the power system and the quality of power supply; deploying high-precision sensors and ensuring that they have good reliability and anti-interference capabilities can significantly improve the accuracy of data acquisition; the setting of the acquisition frequency has been verified by experiments, and it is neither too low to capture environmental changes nor too high to cause unnecessary computing burden; it helps to balance the system response speed and resource consumption and improve the overall operating efficiency; reliable communication means are adopted during data transmission, and the security and integrity of the data are ensured to prevent data loss or tampering; according to the rate of environmental changes, system response requirements, and data processing The collection frequency is set comprehensively based on multiple factors such as management capabilities, cost-effectiveness, and historical data analysis, so that the system can flexibly respond to different operating scenarios and demand changes, which helps to improve the adaptability and scalability of the system; real-time and accurate environmental data provides strong decision-making support for the operation and regulation of hydropower generators; through data analysis, potential problems can be discovered in a timely manner, targeted regulation strategies can be formulated, and the operation mode and parameter settings of hydropower generators can be optimized, thereby improving power generation efficiency and economic benefits; this step provides a solid data foundation and technical support for the operation and regulation of hydropower generators by scientifically and reasonably setting the collection frequency and continuously acquiring environmental data information, which helps to improve the overall performance and operation efficiency of the power system.

[0078] S2. Using the preset trained key feature extraction model, extract features from the environmental data information collected each time to obtain the key feature vector of the environment;

[0079] The method for constructing the key feature extraction model comprises:

[0080] Collect a large amount of data covering the operating status of hydropower generators under different environmental conditions;

[0081] Clean the collected data to remove outliers, missing values, and noise data; then, normalize the data to ensure that data of different dimensions and ranges can be compared and analyzed on the same scale;

[0082] According to data characteristics and task requirements, a deep learning model is selected as the basic architecture of the key feature extraction model; the deep learning model includes a convolutional neural network, a recurrent neural network, a support vector machine, and an autoencoder;

[0083] Use the processed data as input to train the key feature extraction model; during the training process, adjust the model parameters and optimize the algorithm so that the model can accurately extract key features from the input data;

[0084] Use an independent test set to evaluate the performance of the model and improve the accuracy and generalization ability of feature extraction; the evaluation indicators include precision, recall and F1 score;

[0085] Tune the model based on the evaluation results, including adjusting the model structure, adding regularization terms to prevent overfitting, and using ensemble learning methods to improve stability;

[0086] The trained feature extraction model is deployed to the dynamic control system of the hydroelectric generator to ensure that the system can call the model for feature extraction in real time.

[0087] In this step, the possibility of manual intervention and errors is reduced through the automated feature extraction process, which improves the efficiency and accuracy of data processing. The model can quickly extract key features from a large amount of complex environmental data, providing strong support for subsequent analysis and decision-making. Since the model has been trained and deployed to the system, it can extract features from newly collected environmental data in real time, so that the dynamic control system of the hydropower generator can quickly respond to environmental changes, adjust operating parameters, and ensure the stability of power generation efficiency and safety. The key feature extraction model built with advanced technologies such as deep learning has strong feature learning and generalization capabilities, and can automatically learn and extract key features from data. The key features that have a significant impact on system operation; enable the system to understand the environmental status more accurately and make reasonable control decisions accordingly; by accurately monitoring environmental changes in real time and adjusting the operating parameters of the hydropower generator, it can ensure that the unit operates in the optimal state and avoid energy waste and mechanical failure caused by overload or inefficient operation; it helps to optimize resource utilization and improve economic and environmental benefits; the key feature extraction model has been fully optimized and tuned during the training process and can cope with complex and changeable environmental changes; at the same time, the stability of the model is improved through methods such as ensemble learning, so that the system can still maintain good performance in the face of extreme or abnormal situations, ensuring the safe and stable operation of the hydropower generator.

[0088] S3, performing fluctuation analysis on a plurality of continuous key environmental feature vectors to obtain an environmental fluctuation index;

[0089] The method for obtaining the environmental fluctuation index comprises:

[0090] Collect key feature vectors of the environment within a certain time window;

[0091] For each key environmental feature vector, the difference between its adjacent vectors is calculated, and the difference result is used to characterize the change in environmental conditions between adjacent time points; the difference result refers to the difference in the key environmental feature vectors between adjacent time points, which is expressed by calculating the Euclidean distance between the two vectors. The difference result reflects the change in environmental conditions between adjacent time points.

[0092] In order to measure the amplitude of fluctuation, the sum of the absolute values ​​of all difference results and the mean of the sum of squares are calculated; based on the calculation results of the fluctuation amplitude, the environmental fluctuation index is defined; the fluctuation amplitude calculated above is normalized to fall within a fixed range to facilitate subsequent threshold comparison; the environmental fluctuation index calculation formula is:

[0093]

[0094] Wherein, FI represents the environmental fluctuation index, reflecting the average degree of fluctuation; V i Represents the key feature vector of the environment at the i-th time point; ∥V i+1 -V i ∥ means V i+1 and V i The Euclidean distance between them is used to calculate the difference between two vectors; n represents the number of data points in the time window.

[0095] In this step, by collecting the key environmental feature vectors within a certain time window, the dynamic changes of environmental conditions can be monitored in real time; by calculating the differences between adjacent vectors, and further calculating the sum of the absolute values ​​of these differences and the mean of the sum of squares, the fluctuation amplitude of environmental conditions can be quantified; this quantification method enables the degree of environmental change to be measured specifically and objectively, providing strong support for subsequent decision-making analysis; the fluctuation amplitude is normalized so that the fluctuation index falls within a fixed range, greatly improving the comparability and practicality of the fluctuation index; the normalized fluctuation index is not only convenient for threshold comparison, but also for horizontal and vertical comparative analysis at different times and under different environmental conditions; by defining the environmental fluctuation index and substituting the measurement results of the fluctuation amplitude for calculation, a comprehensive evaluation of the fluctuation of environmental conditions is achieved; the final environmental fluctuation index can be used as an important basis for decision support; in the fields of environmental monitoring, disaster warning, resource management, etc., the level of the environmental fluctuation index can directly reflect the stability and risk level of environmental conditions, and provide timely and effective information support for relevant decision makers.

[0096] S4, comparing the environmental fluctuation index with a preset fluctuation threshold;

[0097] The factors affecting the setting of the preset fluctuation threshold include:

[0098] Physical characteristics of hydroelectric generators: Hydroelectric generators of different capacities and powers have different sensitivities and tolerances to environmental changes; large-capacity, high-power hydroelectric generators need to set lower fluctuation thresholds in order to identify and respond to environmental changes earlier and prevent overload or mechanical failure; the design and manufacturing standards of hydroelectric generators will also affect their operational stability and adaptability to environmental changes; hydroelectric generators that meet higher standards may have better self-regulation capabilities, so the fluctuation threshold can be appropriately increased;

[0099] The scope and frequency of changes in environmental factors: By analyzing historical environmental data, understanding the scope and frequency of changes in various natural factors can help determine a reasonable fluctuation threshold; if the water flow velocity in a certain area changes frequently and with large amplitudes, a lower fluctuation threshold may need to be set to ensure timely response; considering the impact of extreme weather conditions on the operation of hydropower generators, environmental fluctuations under these conditions may be more severe, requiring special attention and appropriate adjustment of the fluctuation threshold;

[0100] Demand and stability of the power system: The power system has strict requirements on the power supply quality, including voltage stability, frequency stability, etc.; as an important power generation equipment, the stability of the operating state of the hydropower generator directly affects the power supply quality of the power system; therefore, when setting the fluctuation threshold, it is necessary to consider how to ensure the stable operation of the hydropower generator to meet the needs of the power system; the operating efficiency of the hydropower generator directly affects the economic benefits; too high a fluctuation threshold will cause the hydropower generator to be unable to adjust the operating parameters in time when the environment changes, resulting in energy waste; while too low a fluctuation threshold will increase the frequency and cost of regulation; therefore, when setting the fluctuation threshold, it is necessary to weigh the relationship between economic benefits and operational stability;

[0101] Safety performance and reliability: Hydroelectric generators face the risk of mechanical failure when operating under extreme environmental conditions. In order to avoid mechanical failures caused by environmental changes, it is necessary to set a reasonable fluctuation threshold so that timely control measures can be taken. Considering the redundant design of the hydroelectric generator system can improve the reliability and fault tolerance of the system to a certain extent, thereby providing a certain safety margin when setting the fluctuation threshold.

[0102] In this step, by setting a reasonable fluctuation threshold, the system can quickly identify the significance of environmental changes; reasonable fluctuation threshold setting helps to maintain the stable operation of the hydroelectric generator; when the environmental fluctuation is small, the system maintains the current operation mode unchanged, avoiding unnecessary parameter adjustment to interfere with the stability of the system; when the environmental fluctuation is significant, the system can quickly adjust the operating parameters to ensure that the hydroelectric generator operates in the optimal state, thereby improving the power supply quality and stability of the entire power system; by weighing the relationship between economic benefits and operational stability to set the fluctuation threshold, it is possible to maximize the energy utilization efficiency while ensuring the stable operation of the hydroelectric generator; avoiding energy waste caused by too high fluctuation thresholds and increased regulation costs due to too low fluctuation thresholds, making the operation of the hydroelectric generator more economical and efficient; reasonable fluctuation threshold setting also helps to improve the safety performance of the hydroelectric generator; under extreme environmental conditions, the system can identify potential risk factors earlier and take corresponding regulatory measures to avoid the occurrence of mechanical failures; at the same time, considering the redundant design of the hydroelectric generator system, a certain safety margin is provided when setting the fluctuation threshold, further enhancing the reliability and fault tolerance of the system.

[0103] S5. In response to the environmental fluctuation index exceeding the preset fluctuation threshold, the newly acquired environmental key feature vector is input into the hydroelectric generator environmental impact analysis model to obtain the hydroelectric generator environmental impact index;

[0104] The mathematical framework of the hydroelectric generator environmental impact analysis model includes:

[0105] Input layer: The input is the key feature vector of the environment, representing different environmental parameters;

[0106] Hidden layer: The hidden layer contains multiple neurons, each of which receives weighted input from the input layer or the previous hidden layer and applies a nonlinear activation function to calculate the output;

[0107] Output layer: The output layer usually contains one or more neurons, which are used to output the environmental impact index of the hydroelectric generator; this index may be a single value that integrates multiple environmental factors, or it may be a multidimensional vector that represents different aspects of environmental impact;

[0108] The method for constructing the hydroelectric generator environmental impact analysis model comprises:

[0109] Collect historical data sets containing environmental data and hydropower generator operation data; these data should cover different environmental conditions and operating conditions to ensure the generalization ability of the model;

[0110] Preprocess the collected data, including removing outliers, filling missing values, and standardizing or normalizing data to improve the efficiency and accuracy of model training;

[0111] Selecting a machine learning model as the infrastructure of the hydroelectric generator environmental impact analysis model; the machine learning model includes linear regression, random forest regression, gradient boosting tree, multi-layer perceptron, convolutional neural network and recurrent neural network;

[0112] Use the preprocessed dataset to train the selected model; during the training process, the model's hyperparameters need to be adjusted to optimize the model's performance;

[0113] An independent test set is used to validate the model and evaluate its generalization ability on unknown data;

[0114] Tune the model based on the validation results, including adjusting the model structure, replacing a more appropriate algorithm, or further optimizing feature selection;

[0115] The model is deployed to the actual hydroelectric generator operation and control system to achieve dynamic control based on environmental changes.

[0116] In this step, the key characteristic vectors of the environment can be analyzed in real time, and the impact of environmental changes on the operating efficiency of the hydroelectric generator can be accurately predicted; by timely adjusting the operating parameters, it is ensured that the hydroelectric generator can maintain the optimal operating state under different environmental conditions, thereby improving the overall power generation efficiency; by predicting the environmental impact index, the model can identify potential safety risks in advance and trigger corresponding control measures to avoid overload operation or mechanical failure of the unit, thereby enhancing the operating safety of the hydroelectric generator; the model can intelligently adjust the output of the hydroelectric generator according to environmental fluctuations to avoid energy waste caused by excessive power generation when environmental conditions are unfavorable; at the same time, when environmental conditions are favorable, by optimizing the operating parameters, maximize energy utilization and improve economic benefits ; The model was deployed to the actual hydroelectric generator operation and control system to realize automatic control based on environmental changes; this not only reduced the burden of manual monitoring and adjustment, but also improved the accuracy and timeliness of control, making the operation of the hydroelectric generator more intelligent and efficient; through real-time analysis and control, the model helps to maintain the stability of the hydroelectric generator's operating state; when the environment fluctuates greatly, it can quickly adjust the operating parameters to cope with the changes, reduce system fluctuations and unstable factors caused by environmental changes, and improve the stability and reliability of the entire power system; by constructing and applying the environmental impact analysis model, the efficient, safe and stable operation of the hydroelectric generator is achieved, providing strong support for the widespread application and sustainable development of renewable energy.

[0117] S6. Optimizing the environmental impact index of the hydroelectric generator in a preset operation mode mapping space to obtain the optimal operation mode of the hydroelectric generator under the real-time environmental conditions; and regulating the operation parameters of the hydroelectric generator according to the optimal operation mode;

[0118] The optimal operation mode refers to the operation mode in which the hydroelectric generator can operate in the most efficient and safest state under the current environmental conditions. The method for constructing the preset operation mode mapping space includes:

[0119] Clarify the operating requirements and limitations of hydroelectric generators under different environmental conditions; including improving the operating efficiency of hydroelectric generators within the permitted range, ensuring the safety of hydroelectric generators under various environmental conditions and the economic benefits of the power market;

[0120] Collect the operating data of the hydroelectric generator under different environmental conditions, including environmental parameters and corresponding operating parameters; pre-process the collected data to remove outliers and missing values ​​to ensure the accuracy and completeness of the data; extract key features that have a significant impact on the operation of the hydroelectric generator from the original data;

[0121] The operation modes are divided into different categories according to the operation requirements and limitations of the hydroelectric generators;

[0122] Define corresponding operating parameter settings for each mode to ensure that the hydropower generator can operate stably and meet the operating requirements in this mode;

[0123] The key feature vector of the environment is used as the input space, the operation mode is used as the output space, and a mapping relationship from the input space to the output space is constructed;

[0124] Use machine learning methods to train historical data and establish a mapping relationship between key environmental feature vectors and operating modes;

[0125] The model is verified and optimized through methods such as cross-validation to ensure that the model can accurately map the key feature vectors of the environment to the corresponding operating mode.

[0126] In this step, by analyzing the key feature vectors of the environment in real time and finding the best solution in the preset operation mode mapping space, it can ensure that the hydropower generator always operates in the best mode under complex and changeable environmental conditions; it helps to maximize the operating efficiency of the hydropower generator and reduce energy waste; the construction of the operation mode mapping space fully considers the operating limitations and safety requirements of the hydropower generator; in the real-time control process, the system can automatically avoid potential risks such as overload, vibration, overheating, etc., to ensure that the hydropower generator can operate safely under various environmental conditions; combined with the real-time electricity price in the power market and the maintenance cost of the equipment, the operation mode mapping space can optimize the operation strategy of the hydropower generator to maximize the economic benefits; by reducing unnecessary downtime and maintenance costs, while maximizing power generation and improving the overall economic benefits of power production; using machine learning methods to establish a mapping relationship between environmental key feature vectors and operating modes, realizing intelligent regulation of hydropower generators; being able to automatically adapt to environmental changes, reduce manual intervention, and improve the accuracy and real-time performance of regulation; verifying and optimizing the model through methods such as cross-validation to ensure the accuracy and robustness of the mapping model; this enables the system to maintain stable regulation performance in the face of abnormal or extreme environmental conditions, avoiding system collapse or loss of control; through dynamic regulation methods, it is possible to maximize the use of hydropower resources, reduce dependence on traditional energy, and promote the optimization of energy structure and sustainable development of the environment.

[0127] More specifically, the classification and definition of operating modes are based on the operating requirements and limitations of hydroelectric generators under different environmental conditions; specific classifications include but are not limited to:

[0128] High-efficiency operation mode: maximize power generation efficiency when environmental conditions permit;

[0129] Safe operation mode: ensures safe operation of the hydropower generator when environmental conditions are unstable or there are potential risks;

[0130] Economic operation mode: Combine the real-time electricity price in the power market and the maintenance cost of equipment to optimize the operation strategy and maximize the economic benefits;

[0131] Low-load operation mode: When environmental conditions are poor or demand is low, the output is reduced to avoid unnecessary energy waste.

[0132] S7, in response to the environmental fluctuation index not exceeding the preset fluctuation threshold, maintaining the real-time operation mode of the hydroelectric generator unchanged;

[0133] If the environmental fluctuation index is lower than or equal to the preset fluctuation threshold, the system determines that the current environment is in a relatively stable state and will not have a significant impact on the operating efficiency and safety of the hydropower generator;

[0134] After confirming that the environment is stable, the system will not trigger the adjustment mechanism for the operating parameters of the hydroelectric generator; it will maintain the current real-time operating mode of the hydroelectric generator unchanged to ensure that the generator continues to operate in a stable state;

[0135] Although the current environment is stable, the system will not stop collecting and monitoring environmental data; the system continues to obtain environmental data information at the location of the hydroelectric generator at the set collection frequency, and is ready to respond in a timely manner when the environment changes;

[0136] By reducing unnecessary control operations, the S7 step helps optimize resource utilization; during stable environmental conditions, the hydropower generator can maintain efficient and stable operation, reducing energy loss and mechanical wear caused by frequent adjustments, thereby improving overall economic benefits and equipment life;

[0137] Although the current environment is stable, the system always remains highly vigilant and is ready to respond quickly when the environmental fluctuation index exceeds the preset threshold; this ensures that the system can make rapid adjustments in the face of sudden environmental changes and ensure the operating efficiency and safety of the hydropower generator.

[0138] In this step, when the environmental fluctuation index does not exceed the preset threshold, that is, when the environment is relatively stable, the system maintains the real-time operation mode of the hydroelectric generator unchanged, avoiding unnecessary adjustment of operating parameters; reducing energy loss and mechanical wear that may be caused by frequent adjustment of operating parameters, thereby optimizing resource utilization and improving power generation efficiency and economy; maintaining the continuous operation of the hydroelectric generator in a stable state helps to reduce equipment stress caused by frequent operations, reduce the risk of mechanical failure, and thus extend the service life of the equipment; continuous monitoring ensures that the system can grasp the environmental dynamics in real time. Once the environmental fluctuation index exceeds the preset threshold, the system can respond quickly and adjust the operating parameters of the hydroelectric generator, thereby ensuring the operating efficiency and safety of the generator; by reducing unnecessary control operations and reducing energy consumption, as well as extending the service life of equipment, the implementation of this step helps to improve the overall economic benefits of the hydropower station; this step embodies the application of intelligent management in hydropower production, and through automated monitoring and intelligent decision-making, it realizes precise control and optimization adjustment of the operating status of power generation equipment; this helps to promote the digital transformation and intelligent upgrading of the hydropower industry.

[0139] Embodiment 2: Figure 3 As shown, the dynamic control system of a hydroelectric generator based on environmental changes of the present invention specifically includes the following modules:

[0140] The environmental data collection module continuously obtains environmental data information of the location of the hydroelectric generator according to the set collection frequency;

[0141] The feature extraction module uses the preset trained key feature extraction model to extract features from the environmental data information collected each time and obtain the key feature vector of the environment;

[0142] The environmental fluctuation analysis module performs fluctuation analysis on multiple consecutive environmental key feature vectors to obtain the environmental fluctuation index;

[0143] A threshold comparison module compares the environmental fluctuation index with a preset fluctuation threshold;

[0144] The environmental impact analysis module, in response to the environmental fluctuation index exceeding the preset fluctuation threshold, inputs the latest acquired environmental key feature vector into the hydroelectric generator environmental impact analysis model to obtain the hydroelectric generator environmental impact index;

[0145] The operation mode optimization module optimizes the environmental impact index of the hydroelectric generator in the preset operation mode mapping space to obtain the optimal operation mode of the hydroelectric generator under the real-time environmental conditions; and adjusts the operation parameters of the hydroelectric generator according to the optimal operation mode; in response to the environmental fluctuation index not exceeding the preset fluctuation threshold, the real-time operation mode of the hydroelectric generator is kept unchanged.

[0146] Through continuous environmental data collection and real-time analysis, the system can quickly respond to environmental changes and adjust the operating mode of the hydroelectric generator in a timely manner, effectively avoiding unit overload or energy waste caused by environmental changes;

[0147] Using advanced technologies such as machine learning or deep learning to extract features and analyze environmental fluctuations, we can achieve intelligent processing of complex environmental factors, improve the accuracy and scientificity of regulation, and reduce the uncertainty caused by manual intervention and empirical judgment.

[0148] The system dynamically adjusts the operating mode of the hydroelectric generator to adapt to different environmental conditions, enhancing the adaptability and flexibility of the equipment and ensuring efficient and stable operation under different environmental conditions. By analyzing environmental fluctuations in advance and predicting their impact on the hydroelectric generator, the system can take preventive measures to avoid potential failures and improve the reliability and safety of the equipment.

[0149] The output is automatically reduced when the water flow rate decreases, avoiding unnecessary energy waste and improving energy utilization efficiency. The entire control process is highly automated, reducing the complexity and error rate of manual operations, reducing operation and maintenance costs, and improving management efficiency.

[0150] In summary, the dynamic control system significantly improves the operating efficiency and stability of the hydropower generator in a complex and changing environment by regulating the operating mode of the hydropower generator in real time, intelligently and adaptively.

[0151] The various variations and specific embodiments of the method for dynamic control of a hydroelectric generator based on environmental changes in the aforementioned embodiment 1 are also applicable to the dynamic control system for a hydroelectric generator based on environmental changes in the present embodiment. Through the aforementioned detailed description of the method for dynamic control of a hydroelectric generator based on environmental changes, those skilled in the art can clearly understand the implementation method of the dynamic control system for a hydroelectric generator based on environmental changes in the present embodiment. Therefore, for the sake of brevity of the specification, it will not be described in detail here.

[0152] In addition, the present application also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor. The transceiver, the memory, and the processor are respectively connected via a bus. When the computer program is executed by the processor, each process of the above-mentioned method for controlling output data is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0153] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for dynamic control of a hydroelectric generator based on environmental changes, characterized in that: The method comprises: Continuously obtain environmental data information of the location of the hydroelectric generator according to the set collection frequency; Using the preset trained key feature extraction model, feature extraction is performed on the environmental data information collected each time to obtain the key feature vector of the environment; Perform fluctuation analysis on multiple consecutive key environmental feature vectors to obtain the environmental fluctuation index; Comparing the environmental fluctuation index with a preset fluctuation threshold; In response to the environmental fluctuation index exceeding a preset fluctuation threshold, the newly acquired environmental key feature vector is input into the hydroelectric generator environmental impact analysis model to obtain the hydroelectric generator environmental impact index; The environmental impact index of the hydroelectric generator is optimized in the preset operation mode mapping space to obtain the optimal operation mode of the hydroelectric generator under the real-time environmental conditions; and the operation parameters of the hydroelectric generator are adjusted according to the optimal operation mode; In response to the environmental fluctuation index not exceeding the preset fluctuation threshold, the real-time operation mode of the hydroelectric generator is maintained unchanged.

2. The method for dynamic control of a hydroelectric generator based on environmental changes according to claim 1, characterized in that: The environmental fluctuation index calculation formula is: Wherein, FI represents the environmental fluctuation index, reflecting the average degree of fluctuation; V i Represents the key feature vector of the environment at the i-th time point; ∥V i+1 -V i ∥ means V i+1 and V i The Euclidean distance between them is used to calculate the difference between two vectors; n represents the number of data points in the time window.

3. The method for dynamic control of a hydroelectric generator based on environmental changes according to claim 1, characterized in that: The method for constructing the key feature extraction model comprises: Collect data on the operating status of hydroelectric generators under different environmental conditions; Clean the collected data to remove outliers, missing values, and noise data; Selecting a deep learning model as the infrastructure of the key feature extraction model; the deep learning model includes a convolutional neural network, a recurrent neural network, a support vector machine, and an autoencoder; Use the processed data as input to train the key feature extraction model; Use an independent test set to evaluate the performance of the model; Tune the model based on the evaluation results; The trained feature extraction model is deployed to the dynamic control system of the hydroelectric generator.

4. The method for dynamic control of a hydroelectric generator based on environmental changes according to claim 1, characterized in that: The influencing factors for setting the preset fluctuation threshold include the physical characteristics of the hydroelectric generator, the range and frequency of changes in environmental factors, the demand and stability of the power system, and the safety performance and reliability.

5. The method for dynamic control of a hydroelectric generator based on environmental changes according to claim 1, characterized in that: The method for constructing the hydroelectric generator environmental impact analysis model comprises: Collect historical data sets containing environmental data and hydroelectric generator operation data; Preprocess the collected data, including removing outliers, filling missing values, and standardizing and normalizing the data; Selecting a machine learning model as the infrastructure of the hydroelectric generator environmental impact analysis model; the machine learning model includes linear regression, random forest regression, gradient boosting tree, multi-layer perceptron, convolutional neural network and recurrent neural network; Use the preprocessed dataset to train the selected model and adjust the model’s hyperparameters; An independent test set is used to validate the model and evaluate its generalization ability on unknown data; Tune the model based on the validation results; The model is deployed to the actual hydropower generator operation and control system.

6. The method for dynamic control of a hydroelectric generator based on environmental changes according to claim 1, characterized in that: The method for constructing the preset operation mode mapping space includes: Identify the operating requirements and limitations of hydroelectric generators under different environmental conditions; Collect the operating data of the hydroelectric generator under different environmental conditions, including environmental parameters and corresponding operating parameters; pre-process the collected data to remove outliers and missing values; According to the operation requirements and limitations of the hydroelectric generator, the operation modes are divided into different categories, and corresponding operation parameter settings are defined for each mode; The key feature vector of the environment is used as the input space, the operation mode is used as the output space, and a mapping relationship from the input space to the output space is constructed; Establish the mapping relationship between the key feature vectors of the environment and the operating mode, and construct the operating mode mapping space.

7. The method for dynamic control of a hydroelectric generator based on environmental changes according to claim 1, characterized in that: The factors affecting the setting of the acquisition frequency include environmental change rate, system response requirements, data processing capabilities, cost-effectiveness, historical data analysis, and field conditions.

8. A dynamic control system for a hydroelectric generator based on environmental changes, characterized in that: The system comprises: The environmental data collection module continuously obtains environmental data information of the location of the hydroelectric generator according to the set collection frequency; The feature extraction module uses the preset trained key feature extraction model to extract features from the environmental data information collected each time and obtain the key feature vector of the environment; The environmental fluctuation analysis module performs fluctuation analysis on multiple consecutive environmental key feature vectors to obtain the environmental fluctuation index; A threshold comparison module compares the environmental fluctuation index with a preset fluctuation threshold; The environmental impact analysis module, in response to the environmental fluctuation index exceeding the preset fluctuation threshold, inputs the latest acquired environmental key feature vector into the hydroelectric generator environmental impact analysis model to obtain the hydroelectric generator environmental impact index; The operation mode optimization module optimizes the environmental impact index of the hydroelectric generator in the preset operation mode mapping space to obtain the optimal operation mode of the hydroelectric generator under the real-time environmental conditions; and adjusts the operation parameters of the hydroelectric generator according to the optimal operation mode; in response to the environmental fluctuation index not exceeding the preset fluctuation threshold, the real-time operation mode of the hydroelectric generator is kept unchanged.

9. An electronic device for dynamically controlling a hydroelectric generator based on environmental changes, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and characterized in that: When the computer program is executed by the processor, the steps in the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 7 are implemented.

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